Time-varying magnetic field gradients are essential for signal preparation and encoding in magnetic resonance imaging and spectroscopy. Most MR methods rely on highly accurate gradient time-courses for correct signal encoding and suffer from artifacts when significant deviations from the prescribed time-courses occur. In practice, effective gradient waveforms usually do differ somewhat from the ideal shapes defined in the underlying pulse sequence. These deviations are largely due to a variety of hardware imperfections including bandwidth limitations of gradient amplifiers, eddy currents induced in gradient coils and in other conducting structures of the scanner, field fluctuations caused by mechanical vibrations after gradient switching, and thermal variation in hardware components. Slight perturbations can also stem from physiologically induced fields that originate in the subject under examination or from magnetic sources and currents external to the MR system. Besides further hardware optimization, the most common ways of addressing dynamic field imperfections are precompensation of gradient waveforms and postcorrection of acquired data. Both of these options are most feasible for mechanisms of perturbation that are reproducible and can be accurately modeled (see Vannesjö S J et al., Gradient system characterization by impulse response measurements with a dynamic field camera. Magn Reson Med 2013; 69: 583-589, and references cited therein).
According to linear systems theory, the above mentioned approach should permit jointly representing all the response mechanisms that are linear and time-invariant (LTI). A net gradient impulse response function (GIRF) should hence incorporate influences on the gradient waveform between the console and the magnet bore. This would include amplifier and coil characteristics as well as eddy currents and vibration-induced fields, without the need to consider individual underlying mechanisms. Knowledge of the comprehensive GIRF could form the basis of advanced pre-emphasis and serve for quality assurance purposes. It could also yield more accurate estimates of effective k-space trajectories for image reconstruction and of other encoding parameters such as b-values in diffusion imaging or gradient moments in velocity mapping. The key challenge toward this goal is determining GIRFs accurately, with sufficient bandwidth and frequency resolution, and within reasonable measurement times. Probing the GIRF must generally involve observing a system's response to given gradient input waveforms.
For this purpose, it has been proposed to record the field evolution with a dynamic field camera, i.e. with an array of miniature NMR probes that are operated simultaneously and positioned such as to distinguish different spatial components of interest. In the work done so far, particular emphasis was placed on designing specific gradient input waveforms that are tailored to the subsequent observation with a dynamic field camera (see Vannesjö S J et al., Field camera measurements of gradient and shim impulse responses using frequency sweeps. Magn Reson Med 2014; 72: 570-583, and references cited therein).
However, changes of the gradient impulse response due to various influencing factors such as e.g. thermal changes of components of the gradient system can limit the accuracy and hence applicability of the above proposed method.
Accordingly, it would be desirable to acquire GIRFs in a manner as simple as possible and without the need of interrupting ordinary MR data acquisition.